Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Service Recovery and Competitive Positioning: The
Moderation Effect of Technical Efficiency
Hart O. Awa, Ogwo E. Ogwo and Ojiabo Ukoha
This recipe attempts to provide further insight into service recovery by proposing an
extended framework that captures the main effects between recovery alternatives
and indicators of competitive positioning as well as the moderation effects introduced
by technical efficiency. Two sets of questionnaires with almost similar questions were
administered amongst teachers of Federal Government Colleges and senior officers
of telecommunications firms in the south-eastern Nigeria, where GSM and at least
one CDMA firm have network coverage. Analyzing the data using multiple
regressions, Pearson’s product moment correlation coefficient, and structural
equation modelling; the interactions between the quintiles of the four recovery
alternatives and the indicators of competitive positioning were direct (though some
were inverse) and statistically significant and moderated by technical efficiency. Thus,
the dimensions of service recovery explained varying relationships with competitive
positioning. The paper proposes proactive and relational recovery and specifically
simple and hassle-free recovery, timely and value-creating redress, and realistic user
interface.
Keywords- service recovery; competitive positioning; mobile telephony.
1. Introduction
Managing service quality amidst fierce competition and the increasing recognition for
user-developer interface in most industries is often premised upon the inevitability of
service failures, government’s legislation to enforce corporate responsibility, and the
need to drive profitability through customer loyalty (Sajtos et al., 2010; Slater, 2008;
Michel et al., 2009). The stiff competition and the growing fertile grounds provided to
mobile telephony by the developing economies (Okeleke, 2011; Rebello, 2010;
Mokhlis and Yaakop, 2011), have precipitated operators’ growing appreciation of
service recovery as a critical managerial issue that co-exists with learning from postconsumption experiences and quality performance (Zeithaml and Bitner, 2000;
Zeithaml et al., 1996). The socio-economic indices (see Adepetun, 2011; Uzor, 2011;
Okeleke, 2011; Awa et al., 2014) confirm the ample opportunities and the need for
operators to maintain some kind of predatory and cut-throat manoeuvrability.
Whereas most studies on service recovery used customer satisfaction (see Bitner et
al., 1990; Smith et al., 1999; Michel et al., 2009), word-of-mouth (Kim et al., 2009; La
and Kandampully, 2004), repurchase intentions (East et al., 2007; Davidow, 2000),
and post-complaint behaviour (see Davidow, 2003; East et al., 2007; Michel et al.,
2009) as major dependent variables, rare attempts have directly correlated recovery
alternatives and competitive positioning. Further, studies (e.g., Smith et al., 2009; del
Rio-Lanza et al., 2009; Kim et al., 2009; Davidow, 2003; Michel et al., 2009) on
organizations’ response to customer complaints seem to have paid less attention on
the construct of user-developer interface as an instrument for resolving customer
ordeals. Although competitive positioning (see Porter, 1980; Aaker, 1998; Drucker,
1993; Teece, 2000; Thompson and Strickland, 1997) and user-developer interface
(see Vargo and Lusch, 2004; Prahalad and Ramaswamy, 2004) had been
________________________________
Hart O. Awa, Ph.D, University of Port Harcourt, Nigeria
Ogwo E. Ogwo, Ph.D, Abia State University, Uturu, Nigeria
Ojiabo Ukoha, Ph.D, University of Maryland, USA
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
extensively studied in other contexts in attempts to reposition customer values via
difficult-to-copy distinctiveness, the limited inquiries that have factored them into the
recovery framework(s) create scholarly gaps.
Scholars (Porter, 1980; Aaker, 1998; Thompson and Strickland, 1997) propose that
in most competitive industries, firms use knowledge capital more than traditional
resources to build customer-endorsed and difficult-to-copy uniqueness. Further,
there is a growing cognitive and behavioural change emphasizing user-developer
interface as espoused by earlier political philosophers (e.g., Jean Jacques Rousseau
and John Locke) and the neo-Marxists, Kotlerite’ doctrine, post-Fordism, Foucault’s
government, and post-Maussian gift giving (Vargo and Lusch, 2004; Prahalad and
Ramaswamy, 2004). Even though the scholarly contributions on recovery facilitate
the understanding of the general concept and the universal beliefs on its (recovery)
benefits, scholars (e.g., Mittal and Kamakura, 2001; Bennett et al., 2005; Wash et
al., 2008; McMullan, 2005; Sharma et al., 1981) propose that several moderator
variables are often treasured for serving as the predictors of the relationship between
a given set of dependent and independent variables. In the contexts of mobile
telephony (Gerpott et al., 2001) and wider service studies (see Pritchard, 2003; Yoon
and Uysal, 2005; Knox and Walker, 2001), significant connects exist between client
loyalty, satisfaction, and motivation.
However, because these connects are a part of the measures of competitive
positioning, conventional wisdom implies that some moderator variables play out in
the present study. Specifically in service failure recovery, scholars (see Zeithaml and
Bitner, 2000; Bitner et al., 1990) found that service failure and the manner of
response are major moderators in the recovery-loyalty link. Though yet to be
empirically tested in the context of recovery, technical efficiency (Kompas, 2004;
Alvarez and Crespi, 2003; Gumbau-Albert and Joaquin, 2002), firm’s size (Alvarez
and Crespi, 2003; Kumar, 2003; Kurshev and Strebulaev, 2005; Gumbau-Albert and
Joaquin, 2002; Rajart and Zingales, 1998), consumer characteristics (Mittal and
Kamakura, 2001), environmental complexity and munificence (Dess and Origer,
1987), and environmental dynamism (Priem, 1990) are conventionally proposed to
play moderating role in recovery frameworks. Factoring in moderation variables into
the service recovery framework of mobile telephony is quite timely bearing in mind
that scholars (Gabriela and Badii, 2010; Apulu et al., 2011; Babaita, 2010; del-RioLanza et al., 2009; Gerpott et al., 2001) recognize the significant socio-economic
roles of mobile telephony in reducing rural-urban migrations and contributing to other
sectors’ developments in times of risks, disasters, and/or emergencies.
Therefore, this recipe attempts to provide further insight into service recovery by
proposing an extended framework that captures the main effects between recovery
alternatives and indicators of competitive positioning as well as the moderation
effects introduced by technical efficiency. Technical efficiency was chosen as the
contextual factor because scholars (Zmud, 1987; Zhu et al., 2003) propose that its
strength influences the service representatives’ perception of the environment and
the likelihoods to build experiential knowledge and competitive advantage. Some
other scholars (Estelami and De Maeyer, 2002; McCollough et al., 2000) propose
that technical efficiency is synonymous with the calibre of personnel and ultimately
shapes firm’s operating costs and profitability as well as customer delights and
progress in the loyalty ladder.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
2. Literature review
a. Service failure recovery (SFR)
Many baseline philosophies provide the foundational framework to service failure recovery;
they offer explanatory and predictive lenses to people’s reactions. Amongst such
theories/laws are the golden rule, equity and social justice theory, justice dimensional
theory, ethical relativism, ethical egoism theory, utilitarian theory, and the law of
distributive justice. Principally, these philosophical frameworks propagate fairness, justice,
egalitarianism, and improved standard of living, and so, their application to service recovery
is worthwhile bearing in mind the goodwill they build amidst competition. For instance,
ethical relativism considers one universal standard or set of standards that judge(s) actions;
ethical egoism promotes long-run greatest possible balance of good over evil; utilitarianism
emphasizes one’s action making the greatest good for the greatest number of people;
golden rule entails dealing with others in a manner you would want them to deal onto you;
and distributive justice discourages too much wealth at the expense of others, especially the
poor (Lawrence et al., 2002).
Implicit is that every consumption is associated with the probabilities of negative or positive
events (Oliver, 1981). Consumption explains the subjective pre-purchase/pre-trial
functional, social and/or psychological beliefs (shaped by Bass model; see Mahajan et al.,
1990; Oslon and Dover, 1979) about a service/provider, which serve as references against
which actual performance is judged (Zeithaml et al., 1993). Scholars (see Zeithaml et al.,
1993; Parasuraman et al., 1988; Woodruff et al., 1983; LaBarbera and Mazursky, 1983)
propose that when the subjective assessment of service delivery attributes falls short of the
perceived ideals, inequity, perceived injustice, and of course complaints result. Satisfaction
espouses input-output relations; the consumer weighs the perceived contributions/input
scores (economic, time, social, energy, and psychological costs) against the perceived
rewards/output scores (cash refunds, apology, replacements, manner of addressing the
issues) and compare them with those of referent others in similar cases to ensure equity
(Awa et al., 2014; Hoffman et al., 2000). The differential expectations or estimates that
often result explain why industry players offer different services and still remain
competitive. Often the recovery team displays justice and fairness to avert the affected
customer taking actions (public and/or private) against the provider in order to restore
harmony amongst his cognitive elements.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Whereas some studies (Brown et al., 1996; Andreassen, 2001; Berry et al., 1990) suggest
that nothing pleases a customer more than a reliable, first-time and error-free service;
others (Michel et. al., 2009; Smith et al., 2009; Spreng et al., 1995; Etzel and Silverman,
1981) assume that the inevitability of errors triggers recovery effort(s) to compensate the
affected customer in a manner at least equal to his perceived ordeals. Service failures
abound, especially in the telecommunications industry where Momo (2012) reported that
Nigeria’s leading Opinion Polling and Research Organization in partnership with The Gallup
Organization (USA) found that 64% of mobile phone users use more than one line in order
to circumvent network failures. Therefore, service recovery defines a firm’s second chance
to deal with perceived service failures (East et al, 2007; Smith et al., 2009), to promote
customer retention and to dissuade switching behaviour, sharing of ugly experiences, or
even challenging the firm through consumer right organizations, activists, or legal systems
(Sajtos et al., 2010; Zeithaml and Bitner, 2000). Hart et al. (1990) define service recovery as
strategies used to resolve and to learn from service failures in order to (re)establish
reliability and trust in the eyes of consumers. It limits the harms of a service failure rather
than impressing the customer when something has gone wrong (Michel et. al., 2009;
Gonzalez et al., 2010; Kim et al., 2009).
Further, service recovery is a managerial action aimed not only at resolving the problems
that caused the service failures (Michel, 2001; Smith et al., 2009) but also at seeking out,
dealing with, and learning from, perceived service failures (Tax et al., 1998; Slater, 2008)
even when they are not reported (Kim et al., 2009). The salient point raised is proactive
recovery, which indeed is informed by studies (Smith et al., 2009; Hoffman et al., 1995)
showing that only a minority of disgusted customers actually complains and that most
recoveries do not lead to customer satisfaction. Non-complainants are discouraged by the
emotional stress, anger, and disappointment of previous recovery experiences; they deny
operators the opportunity to learning from the lessons and experiences of handling such
failures (Edmondson, 2011; East et al., 2007) and often pose economic burden since low
employee morale (in extreme cases resignation) and poor corporate reputation may result
as well as the affected consumers boycotting the product and spreading negative word-ofmouth (McGrath, 2011; Edmondson, 2011). Therefore, Lewis (1996) notes that resolving the
problem(s) at the point of encounter minimizes negative outcomes of a service failure.
Further, the definitions connote that service failure is an antecedent of service recovery; a
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
critical moment of truth intended to restore reputational strides (Berry and Parasuraman,
1991; East et al., 2007). They imply service recovery as simply much broader and more
proactive than complaint management though both focus on service failure encounter.
While both base on provider’s reaction to customer complaints, service recovery also
addresses service failure on time before the customer deems it necessary to complain
(Grewal et al., 2009; Michel et. al., 2009) on accounts that Michel (2008) opines that it is
only when the first opportunity to recover is missed that the customer complains. However,
the strength of service recovery lies largely on the established relationship and the severity
of the service failure. If the original service was really bad, even strong recovery may get the
customers upset and/or discourage favourable likelihoods (Smith et al., 2009; Zeithaml and
Bitner, 2000). Scholars (de Rio-Lanza et al., 2009; Kim et al., 2009; Michel et al., 2009)
theorize that customers who have less commitment to a service provider tend to be more
transaction-focused and expect immediate recovery when transactions fall short of their
ideals; and those with strong commitment expect low recovery on accounts that continual
relationship with a service provider may settle-out the ordeals and turn them even more
satisfied with service performance after recovery. The manner of response to service
failures has the potential to reinforce loyalty, or to exacerbate the situation and encourage
switching (Smith and Bolton, 1998; Slater, 2008).
For instance, good response tones positively impacts on satisfaction, repurchase intentions,
and attitude toward the providers (Davidow, 2003; TARP, 1981). Edmondson (2011) found
that the behaviour of middle managers in hospitals in terms of responding to failures,
encouraging open discussion, welcoming questions, and displaying humility and curiosity
significantly affects satisfaction, referrals, and profitability. Bitner et al. (1990) theorized the
double deviations principles and with the support of other scholars (e.g., Smith and Bolton,
1998; Davidow, 2003) concluded that it is often the provider’s response rather than the
failure itself that causes disgust. This principle draws from ‘service recovery paradox,’ which
posits that graciously recovered customers were much more satisfied than those who had
not encountered any problems with the initial experiences (Etzel and Silverman, 1981; East
et al., 2009; Zeithaml and Bitner, 2000). Further, research (del Rio-Lanza et al., 2009; Michel
et al., 2009) suggests that a good recovery can turn angry, frustrated customers into
loyalists; in other words, it creates more goodwill than if things had gone right abinitio.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
b. Competitive Positioning
Almost every market seems competitive; therefore service recovery borne out of using
assets and competencies to build/hold customer-endorsed values (carving out a distinctive
niche in the customers’ mind) and difficult-to-copy uniqueness (capability gaps) is an
enviable competitive edge in most markets. Perceiving people as a key factor to sustain
competitive advantage amidst environmental change and globalization, Macmillan (1982)
suggests that strategists seek opportunities to upset industry equilibrium, to pursue
strategies that disrupt normal course of industry events and to forge new industry
conditions to the disadvantage of competitors. Firms position themselves ahead of rivals by
offering reliable, timely, and hassle-free recovery to injured customers (Porter, 1980; Aaker,
1998; Drucker, 1993; Teece, 2000). For instance, promulgating unambiguous policies, rules,
structures, and procedures that encourage customers to register service failures; simplifying
contacts to get such issues resolved; and encouraging customer-friendliness, flexibility, and
tactical decisions are ingredients of competitive positioning. Whichever positioning strategy
pursed in recovery, the essential thing is that reasonable number of actual and potential
consumers must endorsed and perceive it as possessing superior perceived values. In other
words, a positioning strategy may be ideal in terms of conformance value (value from firm’s
point of view) but lacks superior perceived value (value from customer’s point of view),
which ultimately makes it difficult, if not impossible, to drag in the desired market
behaviour.
c. The study framework and development of assumptions
Aside the baseline theories earlier mentioned, specific studies (e.g., Bitner et al., 1990;
Johnston and Fern, 1999; Davidow, 2003; Boshoff, 1999; Martin, 1985; Smith et al., 2009;
Smith et al., 1999) provide solid underpinning foundational footing to this recipe. For
instance, Bitner et al. (1990) studied the influence of redress, credibility and attentiveness on
satisfaction and found that fixing the problem (redress), recognizing the problem, employee
response and explanation impact on satisfaction. In their descriptive study, Johnston and Fern
(1999) conceptualized speed, redress, apology, and credibility; and listed out customer ideal
complaint responses without empirically testing actual recovery while Smith et al. (1999)
found timeliness, redress and apology to have indirect effect on satisfaction through
perceived justice. Baer and Hill (1994) showed that redress and credibility positively impact
on satisfaction and attitude toward firms whereas other studies found that timeliness, redress,
and attentiveness impact positively on satisfaction (Estelami, 2000; Conlon and Murray,
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
1996; Bitner et al., 1990; Smith et al., 1999; Ruyter and Wetzels, 2000), word-of-mouth
publicity (Martin and Smart, 1994; Blodgett et al., 1997), repurchase intention (Kelley et al.,
1993; Conlon and Murray, 1996; Martin, 1985), post-complaint behaviour (Davidow, 2000),
and attitude toward service provider (Martin, 1985).
Blodgett et al. (1993) found that facilitation, redress, and attentiveness impact positively on
word-of-mouth and repurchase intentions. A similar interaction resulted when Blodgett et al.
(1997) replicated the study but only alternated facilitation with timeliness in an experiment.
Although Boshoff (1999) and Davidow (2000) independently had six-factor scale of
organizational response, Boshoff did not empirically measure his scale, while Davidow
(2000) isolated the effects of his scale (timeliness, facilitation, redress, apology, credibility,
and attentiveness) on satisfaction and post-complaint behaviour. The strength and
contributions of this paper lies on the fact that although some previous studies focus on the
service sector, they rarely propose frameworks that capture constructs involving the sensory
inputs of the consumers and building of difficult-to-copy operational uniqueness. Further,
contemporary frameworks scarcely measured how technical efficiency plays a moderation
role between recovery alternatives and indicators of competitive positioning.
Technical
efficiency
H5




Facilitation
Redress
Timeliness
User interface
H1abc-H4abc
Competitive
positioning
Figure 1: Research framework on direct and moderating effects between recovery and loyalty
Facilitation
The complaint management theorists (Davidow, 2003; Bitner et al., 1990; Smith et al., 1999)
often talk of the ease with which an injured consumer receives hassle-free and timely
resolutions. Scholars (Kim et al., 2009; Bolfing, 1989; Nyer, 2000) reported that encouraging
complaints and making resolution-mechanism (facilitation) easily accessible impacts
positively on complaints likelihood and negatively on negative customer values. The
probability of the complaint being resolved and the number of contacts expended by a
consumer to get a complaint resolved have negative effects on measures of competitive
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
positioning (Davidow and Leigh, 1998; Kolodinsky, 1992). Further, immediate resolution
(Sajtos et al., 2010) and claims handling, including simple and convenient claim procedures
(Durvasula et al., 2000; Slater, 2008) significantly impact on indicators of competitive
positioning. Other studies (Blodgett et al., 1997; Davidow, 2003) reported that facilitation
shows no effect or negative effect on customer values and market share growth, yet some
others (Fornell and Wernerfelt, 1988; Kolodinsky, 1992) reported otherwise.
An increase in facilitation has no effect on sales growth and distinctiveness; only that it
lowers negative customer values (Davidow, 2000). Other studies (Blodgett et al., 1997;
Bolfing, 1989) supported the findings from the perspective of customer values but disagreed
on sales growth and distinctiveness. The opportunity to present complaints to a patient
listener (facilitation) (Goodwin and Ross, 1992), procedural fairness as manipulated by
expression of feelings (Ruyter and Wetzels, 2000) as well as warranty expectations and
disconfirmation (Halstead et al., 1993) significantly predict customer values, market share
growth, and complaint handling. McColl-Kennedy and Sparks (2003) found that equal
treatment to customers (neutrality) impacts negatively on satisfaction, therefore flexibility is
a distinctive feature of facilitation.
H1a: Disgusted customer’s feelings of on-the-spot assistance in getting his issues addressed
significantly influence firm’s competitive positioning.
H1b: Disgusted customer’s feelings of delayed assistance in getting his issues addressed
significantly influence firm’s competitive positioning.
H1c: Disgusted customer’s feelings of no assistance in getting his issues addressed
significantly influence firm’s competitive positioning.
Redress
Studies (see Ruyter and Wetzels, 2000; Sparks and McColl-Kennedy, 2001) somewhat imply
mixed relationship between redress and indicators of competitive positioning. Actual
redress has a significant effect on perceived complaint response, which ultimately impacts
positively on customer satisfaction and distinctiveness (Ruyter and Wetzels, 2000),
repurchase intentions and sales growth (Sparks and McColl-Kennedy, 2001; Mack et al.,
2000), customer value and word-of-mouth (Blodgett et al., 1997). Though, satisfaction with
personnel claims (redress) primarily drives overall satisfaction, negative relationship exists
between redress and word-of-mouth activity (Davidow and Leigh, 1998; Hoffman et al.,
1995). Other studies (e.g., Hoffman et al., 1995; Sparks and McColl-Kennedy, 2001) found
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
positive relationship between redress and sales growth but a negative relationship between
redress and customer values. Kelly (1979) found that dissatisfied consumers want
replacement whereas Mount and Mattila (2000) extended the studies to full or partial
compensation as opposed to absence of redress and found a significant impact on the
indicators of competitive positioning.
There is a positive relationship between the percentage of financial loss reimbursed and
satisfaction with complaint response (Davidow, 2003; McCollough et al., 2000); thus, partial
compensation rarely creates sales growth and distinctiveness. Blodgett et al. (1997) tested
three levels of redress- full exchange, 50 percent discount, and 15 percent discount
(distributive justice) for tennis shoes that wore out quickly and found that total satisfaction
creates corporate distinctiveness amongst complainants. Further, they found that the main
effect of redress was significant only when paired with high attentiveness (interactional
justice). Goodman and Ross (1992) paired redress, facilitation and apology; and found that
little redress increases the strengths of both facilitation and apology beyond just the main
effects. Fornell and Wernerfelt’s (1988) mathematical model shows that generous
compensation impacts positively on repurchase and sales growth.
Boshoff (1997) found that the higher the compensation, the more satisfaction and
consumer values enjoyed. Supporting this finding, scholars (Megehee, 1994) found that
over-benefits impact positively on satisfaction, repurchase, sales growth, customer value,
and favourable word-of-mouth. Other studies (e.g., Estelami and De Maeyer, 2002; Mack et
al., 2000) contrasted the finding (above-normal compensations do not improve the
indicators of competitive positioning); an indication that caution need be exercised in
service providers’ over-generosity to avoid impairing customer value. Goodwin and Ross
(1992) investigated the difference in redress requirements between pecuniary and nonpecuniary complaints and found that in service delays (no direct financial loss), 10 percent
discount (redress) impacted positively on satisfaction. Similarly, Brown et al. (1996) showed
that offering free-gift wrap (redress) after a situation of lack of attention and slow service
(no explicit loss) has a significant impact on satisfaction.
H2a: Disgusted customer’s feelings of on-the-spot redress significantly influence firm’s
competitive positioning.
H2b: Disgusted customer’s feelings of delayed redress significantly influence firm’s
competitive positioning.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
H2c: Disgusted customer’s feelings of no redress significantly influence firm’s competitive
positioning.
Timeliness
Time is of essence in most recovery exercises, though scholarly evidence seems somewhat
mixed. Studies (Blodgett et al., 1997; Boshoff, 1997; Sajtos et al., 2010) reported that actual
response time is not a significant determinant of competitive positioning rather perceived
response time. Response speed does not impact significantly on repurchase intentions, sales
growth, customer values, distinctiveness or word-of mouth likelihood (Davidow, 2003).
Further, timeliness does not impact on satisfaction or appropriateness of the recovery
rather what counts most is what management responds with and how it is expressed
(Estelami, 2000; Megehee, 1994; Slater et al., 2008). Other studies found that timeliness
impacts positively on corporate image (Clark et al., 1992), attitude, satisfaction, repatronage, and word-of-mouth (Durvasula et al., 2000; Conlon and Murray, 1996) since it is
far more superior to delayed response (Hoffman et al., 1995). Timeliness impacts positively
on procedural justice, which in turn has positive effects on recovery satisfaction, repatronage, and word-of-mouth (TARP, 1981; Smith et al., 1999). Speedy action positively
impacts on satisfaction with delight to organizational response (Estelami, 2000) and claims
handling (Durvasula et al., 2000).
Conlon and Murray (1996) reported that response speed impacts positively on satisfaction
and repurchase intentions whereas Davidow (2000) found it to positively impacts on
satisfaction and word-of-mouth but no effects on repurchase intentions or word-of-mouth
likelihoods. We can then conclude that the strength of response speed in determining
competitive positioning is subject to the industry, customer perception, and product
category. Response speed is only critical in non-pecuniary complaints (Gilly and Gelb, 1982)
and when severely delayed by service representatives (Boshoff, 1997). Though a late
response is significantly inferior to a slightly delayed response, an immediate response is
less effective than a slightly delayed response; thereby raising question on appropriate
timing of recovery. Given that customers’ perception differs; response speed is a critical
factor when delayed beyond expectations. Gurney (1990) opines that a customer of fastfood restaurant appreciates response speed whereas little less speed and little more care
are expected in complex loans. Firm’s speed of response has no effects if not paired with
redress (Clark et al., 1992; Davidow, 2003) because without at least minimal level of redress,
the consumer will be so dissatisfied that timeliness will cease to be critical.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
H3a: Disgusted customer’s feelings of on-the-spot response to the ordeals significantly
influence firm’s competitive positioning.
H3b: Disgusted customer’s feelings of delayed response to the ordeals significantly influence
firm’s competitive positioning.
H3c: Disgusted customer’s feelings of no response to the ordeals significantly influence
firm’s competitive positioning.
User interface
Some 18th century political philosophers (e.g., John Locke and Jean Jacques Rousseau) and
majority of 21st century business scholars and/or practitioners (Lusch and Vargo, 2004;
Prahalad and Ramaswamy, 2004; Zwick et al., 2008; Bonsu and Darmody, 2008) espoused
the concept value co-creation and user interface. By this, firms emphasize on neo-capitalism
and fostering innovations from outside (Gupter and Carpenter, 2009; Zwick et al., 2008);
using customers’ experiential knowledge and ingenuity as a source of competence (Prahalad
and Ramaswamy, 2004) and wealth creation (Bornu and Darmody, 2008). Handling
complaints is a test of customer orientation; therefore, scholars (e.g., Zeithaml and Bitner,
2000; McCollough et al., 2000) posit that involving users in resolving their issues affects
customer value, difficult-to-copy distinctiveness, and market share. Interface with active
and competent customers in resolving service failures has the characteristic of making all
managerial decisions responsive to customer creativity and enhanced socio-economic and
socio-cultural benefits (Arvidsson, 2004).
User interface in a mutually beneficial innovation re-defines and re-engineers competitive
weapons (Ogawa and Pillar, 2006) in terms of minimized risks of product failures and loyalty
defection, stronger relational bond and word-of-mouth publicity, reduced cycle time and
user education, and maximized profits through reduction of large inventory, product returns
and distribution costs (Bae, 2005) and willingness to pay premium price. Boellstorff (2008)
suggests that developers’ ability to innovate and to build competitive advantage amidst
product varieties is subject to interface with customers in a mutually beneficial manner that
conforms to the principles of value creation. Drawing from management theories, Awa et al.
(2011) suggest that firms build competitive advantage when they handle customers’
disgusts in a manner that the customers feel recognized in the survival and growth of the
organization.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
H4a: Disgusted customer’s feelings of being involved on-the-spot in resolving the ordeals
significantly influence firm’s competitive positioning.
H4b: Disgusted customer’s feelings of delay in being involved in resolving the ordeals
significantly influence firm’s competitive positioning.
H4c: Disgusted customer’s feelings of not being involved in resolving the ordeals significantly
influence firm’s competitive positioning.
The Moderator
Technical efficiency explains the service representative’s proficiency in handling customers’
ordeals. Technical efficiency and firm’s size are interwoven; often technical efficiency
increases or decreases with firm’s size (Alvarez and Crespi, 2003; Gumbau-Albert and
Joaquín, 2002). Firm’s growth in terms of efficient recovery of disgusted clients varies with
firm’s size (Rajart and Zingales, 1998; Kurshev and Strebulaev, 2005; Alvarez and Crespi,
2003). Scholars (Estelami and De Maeyer, 2002; McCollough et al., 2000) propose that firm
size affects the calibre of personnel and ultimately firm’s cost and profitability as well as
customer delight and progress in the loyalty ladder. A broader view by other scholars
(Kumar, 2003; Kurshev and Strebulaev, 2005; Kremer, 1993; Rosen, 1982) perceive firm’s
size in the contexts of stock returns, human capital/managerial talents, per-capita wealth,
market penetration, leverage ratio, earnings management, market size, economy of scale
advantage, financial market development, trade credits, liquidity and capital structure,
ownership of physical assets, and employee remuneration and development. Thus, large
firms exhibit higher technical competence and environmental resilience and show more
likelihood to tap from their best practices in dealing with customer issues (Kwon and Zmud,
1987; Zhu et al., 2003).
Conversely, large firms find it more difficult to keep all departments efficiently co-ordinated;
thus the need for Maksimovic and Gordon’s (2002) optimal firm size and non-linear
relationship between size and firm performance. Optimal firm size explains firm’s most
competitive size expressed in per-unit profit given the industry and time. Densmore (1998)
surveyed online service recovery and found about 95 percent of large firms to be deeply
involved against 2 percent of small firms. Other studies (e.g. Alvarez and Crespi, 2003;
Gumbau-Albert and Joaquin, 2002) found that firm’s experiential and technical efficiency
increases with firm’s size and both significantly impact on post-complaint behaviour
(Davidow, 2000; Maxham and Netemeyer, 2002). Zmud (1987) and Zhu et al. (2003) found
that large firms exhibit higher technical competence and show stronger likelihoods to use
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
their experiential knowledge to build competitive advantage. Service representative’s
credibility as a function of firm size impacts significantly on repurchase intentions, postcomplaint satisfaction, competitive positioning, and word-of-mouth (Maxham and
Netemeyer, 2002; Alvarez and Crespi, 2003).
H5: The relationship between the three quintiles of each service recovery instruments and
competitive positioning is moderated by technical efficiency.
3. Population and sampling
Data were drawn from the opinions of 140 service executives of the six existing GSM and
CDMA firms, and 741 federal government-employed teachers (excluding part-time teachers,
PTA teachers, and NYSC teachers) of the ten FGCs/FGGCs in the South-eastern Nigeria.
Although there is a cluster of federal and state ministries and parastatals as well as huge
commercial activities in the cities where FGCs or FGGCs are located, FGCs or FGGCs on their
own play host to major and minor tribes in Nigeria based on federal character policy. The
FGCs and FGGCs studied were those in locations where GSM and at least one CDMA firms
have network interface. Two sets of questionnaire were structured to reflect open-ended
and close-ended questions for the two independent samples; though both were different,
principally they share some similarity in questions bordering on critical issues. The
questionnaires were carefully worded to elicit the right opinions and to compare them.
On accounts that Visafone, the only CDMA firm operating effectively in the South-eastern
Nigeria at least for now (see table 1), is yet to extend its network to Ezemgbo, Leija and
Okposi, the FGCs or FGGCs therein were excluded. The mode of sampling informants was
purposeful random sampling (PRS) and snowball sampling. The logic behind our choice of
PRS was to use experiential knowledge and judgement to select units of analysis (individualbased) that enabled making reasonable comparison in relations to the research objectives
and not for statistical generalization (Mason, 1996). Respondents were given lee-way to
compliment PRS by suggesting other key informants whose opinions could best represent
that of the target community. The analysis was based on 429 valid returned copies of the
questionnaire.
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Table1: Government pay-rolled Teachers in FGGCs and FGCs, and respondents
from service providers
Fed. Govt.
Colleges
1.
FGGC,
Onitsha
2.
FGC,
Nise
FGC,
Enugu
FGGC,
Leija
FGGC,
Owerri
FGC,
Okigwe
FGGC,
Umuahia
FGC,
Ohafia
FGC,
Okposi,
FGGC,
Ezemgbo
Total
3.
4.
5.
6.
7.
8.
9.
10.
No of
Academic
Staff
145
Sample
Size
Service Provider
Customer care
executive
Customer care
Manager
Sample Size
145
MTN
34
10
44
77
77
GLO
25
8
33
158
158
Etisalat
18
6
24
68
XXXX
M-tel
6
2
8
134
134
Air-tel
8
3
11
87
87
Visafone
16
4
20
115
115
35
35
48
XXXX
41
XXXX
908
741
107
33
140
Measurements and purifications
Boshoff’s (2005) RECOVSAT scale was principally adopted to measure the independent
variables. The scale is based on disconfirmation paradigm and measures customer
expectations from a recovery. The scale measures satisfaction with recovery by
communication, empowerment, feedback, atonement, explanation, and tangibles.
However, all the four dimensions of service recovery of this study seem to be captured by
the RECOVSAT scale. Specifically, user interface was captured by communication,
explanations, and empowerment. The RECOVSAT scale as it relates to redress specifies full
and tangible compensation; timeliness- promptness of tangible recovery; and facilitationempowerment and simple resolution of complaints. We create quintiles on the variables to
reflect favourableness or unfavourableness of the individual recovery instruments and to
perform cross-tabulation. The essence is to get a richer understanding of the variables since
some respondents may be unaware of the instruments, and others aware but not informed,
aware and informed, and aware and informed but not yet decided.
Competitive positioning was operationally measured by difficult-to-copy distinctiveness and
superior customer values (Porter, 1985; Thompson and Strickland, 1997). Responses to
batteries of statements were linked to a continuum of 5-point scale (viz., from strongly
agree through strongly disagree). Drawing from previous studies (see Glover and Goslar,
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1993; Kumar, 2003; Kremer, 1993), we measure technical efficiency by proficient human
capital and managerial talents. Although there are other measures proposed by these
scholars, we chose these two because respondents, especially subscribers, will comfortably
provide answers to the questions that bordered on these two items without having to
connect or contact the service providers. Further, we confirm the extent to which the
statement items closely capture the facets of the constructs under investigation. The
constructs have well-developed measures in literature and so, our scales enjoy content
validity. As such, all of them (the constructs) used multi-item scales generated from
literature, and pre-tested on 12 subjects and modified to ensure internal appropriateness
through preliminary focus groups.
Further, a factor analysis of the indicators of the unidimensional constructs of recovery,
technical efficiency, and competitive positioning was performed and all the items reported in
table 2 below surpassed Steven’s (1992) benchmark of 0.60; thus, the table reports only the
observed items that are reasonable indicators of each of the latent variables. Following Cox et
al. (2002), we did data reduction to address cross-loading issues; inter-correlated indicators
and indicators with low commonalities were deleted. Kaiser-Meyer-Olin (KMO=0.885)
measure of sampling adequacy supports the appropriateness of factor analysis (see Mertler
and Vannatta, 2002). The measures in the context of validity met the conditions proposed by
Fornell and Larcker (1981); the condition supports discriminant validity when the average
variance extracted is greater for each factor than the common variance of the two factors
together. Implicit is that the indicators reported in the factor analysis table of the selected
constructs loaded onto separate factors in the expected manner. Also, the instruments
indicated good reliability with composite reliabilities of greater than 0.6 (Bagozzi and Yi,
1988) and the values of Cronbach test surpassing Nunnally’s (1988) benchmark of 0.7.
However, common method bias (CMB) was unavoidable because we work with subjective
opinions. To ensure that CMB was not a significant issue that will compound our results
since the procedural remedies rarely eliminate CMB completely, we test for CMB using the
methods proposed by Podsakoff et al. (2003). The data were analyzed using a single-method
factor model; this involved estimating the model with a single-method, first-order factor
added to the indicators of the constructs. When a common method factor was added, the fit
indices improved slightly. The factor models showed that the adjusted goodness of fit index
(AGFI) = 0.932/0.911; normed fit index (NFI) = 0.925/901; Tucker-Lewis index (TLI) =
0.954/0.923; and root mean residual (RMR) = 0.033/0.037. When common method variance
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
(CMV) was controlled, the coefficients between the constructs remained significant, and the
proportion of the variance explained was almost the same.
Table 2: The study’s scale items
Item
Measurement Scales:
1. Facilitation
When I encounter service failure, service provider(s):

Always tell me how her policy simplifies resolutions

Sort out my issues effortlessly

Offer me manuals to help me resolve the issues myself

Provide readily available customer centre to offer aids

Offer hassle-free structures and procedures for resolutions
Mean
SD
4.74
4.80
4.69
4.80
4.75
1.77
1.64
1.75
1.81
1.77
5.08
4.50
4.14
4.33
4.55
4.19
1.82
1.88
1.96
2.02
1.91
2.13
5.03
4.91
4.68
4.56
4.53
1.83
1.91
1.72
1.79
1.79
4.40
4.14
3.79
4.19
2.12
2.08
2.10
2.15
5.32
5.30
5.21
5.05
5.02
1.58
1.55
1.60
1.51
1.47
4.92
1.72
4.21
4.29
4.01
1.41
1.46
1.32
Alpha
Composite reliability
0.860
0.686
0.919
0.610
2. Redress
When I encounter service failures, the service providers:





Upgrade my services
Offer incentives equal the inconveniences
Offer cash refunds
Replace the service
Engage in service repairs

Charge low for next service delivery
3. Timeliness
When I encounter service failures, the service providers:

Respond promptly

Contact me often to see if I have unreported/unresolved issues

Encourage me to kindly and timely register all my issues

Build problem-solving customer centres

Want me to enjoy full value of my money
4.
User interface



Get me involved to devise a solution
Use my inputs to design a solution
Change firm’s policy because of me

Contact me before any major change in service delivery
0.867
0.636
When I encounter service failures, the service providers:
0.899
0.641
0.861
0.619
5. Competitive positioning
When I feel delighted with the service recovery exercise(s), I



feel the firm has delivered rare value
feel the firm will enjoy stronger competitive strength
feel expectancy confirmed (satisfied)


buy other services from the firm (cross-selling)
provide service-support ideas
6. Technical efficiency
In the event(s) of service failure:

firms that have proficient manpower resolve customer
issues more strategically

experienced service officers are key success factors

experienced service officers engage in follow-up

experienced service officers create customer delights
0.787
0.675
4. Analysis and Results
This paper captures twelve hypothesized main effects and one moderation effect. On
accounts that the measures of the constructs were found reliable and valid (see table 2) and
the goodness of fit criteria of the basic model meet the generally proposed threshold of AGFI,
NFI, TLI, and RMR above, the main effects were analyzed using the Pearson’s product
moment correlation coefficients (R). To ascertain the direction of the relationship in the main
effect, we calculate the standardized coefficients measured by the weighted average. The
moderating effect was tested using the structured equation modelling (SEM). The SEM
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
estimates our theoretical model using SPSS and the approach followed informed the
existence of six regression coefficients since the study investigates the influence of four
recovery instruments on competitive positioning and broke each instrument into quintiles.
Consistent with previous scholars’ (Wold, 1985; Henseler et al., 2009) opinions, this format
rarely leads to estimation problems, and/or improper, or non-convergent results when the
model is complex (that is, large number of latent or manifest variables). However, factoring
technical efficiency into the equation involves testing the general moderating effect and its
direction (whether direct or inverse). SEM analyses the specific moderation effect while
partial correlations test the overall moderation.
4.1 Main effects
The main effects were reported in table 3 below. The table reports on the results of the
correlation matrix; at p < 0.05 for H1 to H12, the values were significant and lend supports to
the fact that the manipulation of the various dimensions of recovery positively impacts on
difficult-to-copy and superior customer values.
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Table 3: Correlation matrix
Basic model effect
Pearson β-value
(R)
pvalue
Hypothesis Decision
H1a
Supported
H1b
supported
On-the-spot assistance –superior customer values
0.277**
0.196
0.000
Delayed assistance- difficult-to-copy quality
0.308**
-0.133
0.000
No assistance - superior customer values
0.237*
-0.148
On-the-spot-assistance- difficult-to-copy quality
0.286**
0.158
Delayed assistance- superior customer values
0.287**
-0.126
0.237*
-0.191
H1c
supported
0.255**
0.097
H21
Supported
Delayed redress - difficult-to-copy quality
0.202*
-0.121
No redress - superior customer values
0.278**
-0.042
H2b
supported
On-the-spot redress- difficult-to-copy quality
0.271**
0.039
Delayed redress- superior customer values
0.220**
-0.061
H2c
supported
No redress - difficult-to-copy quality
0.178*
-0.028
0.322**
0.127
H3a
Supported
Delayed response ---- difficult-to-copy quality
0.298*
-0.110
No response ---- superior customer values
-0.121
H3b
Supported
0.219*
On-the-spot response - difficult-to-copy quality
0.291**
0.141
Delayed response- superior customer values
0.283*
-0.175
No response - difficult-to-copy quality
0.216*
-0.113
On-the-spot involvement-- superior customer values
0.289**
Delayed involvement ---- difficult-to-copy quality
0.268**
0.116
-0.124
No involvement ---- superior customer values
0.288**
0.298*
0.256**
0.231*
No assistance- difficult-to-copy quality
On-the-spot redress
–superior customer values
On-the-spot response
–superior customer values
On-the-spot involvement - difficult-to-copy quality
Delayed involvement- superior customer values
No involvement - difficult-to-copy quality
-0.188
H3c
Supported
H4a
Supported
H4b
Supported
0.164
-0.117
-0.169
H4c
Supported
Note: Correlation is significant at p < 0.05 or 0.01
4.2 Moderation effects
After analyzing the main effects, we test for the moderation effects of technical efficiency
between the dependent and predictor variables. We confirm the general moderating effect of
technical efficiency on all links using the Chi-square difference and then the moderator effect and
its direction for each individual link between the constructs using the multi-group analysis. Chisquare difference of 10.89 with df of 4 shows significant general moderating effect at p < 0.05.
For specifics, we consider the four paths for which moderations occur if the improvement in the
Chi-square from the restricted to the non-restricted model is significant. The Chi-square
difference between the two models is greater than 3.24 at p = 0.05, which indicates the direction
Proceedings of 4th European Business Research Conference
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of the moderation. Observe that in table 4, at least four paths were significant; therefore,
technical efficiency is a moderator at p < 0.01, 0.05, and 0.1 levels. For those links that are
significant, the moderating relationship between the categories of recovery instruments and the
two dimensions of loyalty is stronger for large firms than for medium and small firms at different
p-values.
Table 5: Multi-group analysis
2
Small
Medium
Large
X
ЛX(df=1)
Hypothesis
Decision
Delayed assistance- difficult-to-copy quality
0.267
0.242
0.226
0.201
0.201
0.110
34.001
30.521
2.652
3.001**
No assistance - superior customer values
0.211
0.189
On-the-spot-assistance- difficult-to-copy quality
0.275
41.266
39.120
5.14***
4.23***
0.207
0.174
0.161
Delayed assistance- superior customer values
0.219
No assistance- difficult-to-copy quality
0.189
0.198
0.179
0.151
0.165
36.102
34.114
3.36**
3.16**
Supported
Supported
0.311
0.233
0.223
26.440
2.004
No redress - superior customer values
0.285
0.302
0.241
0.281
0.210
0.234
29.136
41.120
3.123*
5.044***
On-the-spot redress- difficult-to-copy quality
0.314
0.270
0.251
38.304
4.233***
Delayed redress- superior customer values
0.327
0.335
0.285
0.289
0.244
0.265
34.412
36.404
3.372**
3.180**
Not
supported
Supported
Supported
Supported
Supported
Supported
0.255
0.239
0.219
28.140
3.124**
No response ---- superior customer values
0.212
0.259
0.189
0.234
0.171
0.201
29.115
39.266
3.104*
5.144***
On-the-spot response - difficult-to-copy quality
0.234
0.210
0.192
40.071
5.213***
Delayed response- superior customer values
0.278
0.301
0.258
0.263
0.214
0.248
35.212
34.414
3.326**
3.658**
0.195
0.178
0.161
28.210
3.102*
Technical efficiency
On-the-spot assistance –superior customer value
Not supported
Supported
H13
Supported
Supported
ЛX(df=1): 10.161**
Technical efficiency
On-the-spot redress
–superior customer values
Delayed redress - difficult-to-copy quality
No redress - difficult-to-copy quality
H13
ЛX(df=1): 7.271**
Technical efficiency
On-the-spot response
–superior customer values
Delayed response ---- difficult-to-copy quality
No response - difficult-to-copy quality
Supported
H13
Supported
Supported
Supported
Supported
Supported
ЛX(df=1): 6.002**
Technical efficiency
On-the-spot involvement--superior customer values
On-the-spot involvement - difficult-to-copy quality
0.237
0.189
0.199
0.214
0.169
0.172
0.191
24.412
30.132
32.120
2.004
3.140***
3.321***
Delayed involvement- superior customer values
0.249
0.238
0.208
0.218
0.190
0.186
28.112
29.214
3.532**
3.660**
Delayed involvement ---- difficult-to-copy quality
No involvement ---- superior customer values
0.211
0.202
No involvement - difficult-to-copy quality
ЛX(df=1): 5.104**
Notes: * Significant at 0.1; ** significant at 0.05; *** significant at 0.01 levels; H13 is supported because at least four paths are significant
H13
Supported
Not
supported
Supported
Supported
Supported
Supported
Proceedings of 4th European Business Research Conference
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5. Discussion
This inquiry attempts to provide further insight into the model effect (or the relationship)
between the administration of recovery instruments and competitive positioning (main
effects) under the moderation of technical efficiency (moderation effects). Specifically, it
intends to unveil if the links between the several categories of recovery instruments and the
two dimensions of competitive positioning (difficult-to-copy uniqueness and superior
customer values) can be explained by firm’s existing technical efficiency. The equation
shows that the various quintiles of the instruments of service recovery studied were found
significant predictors of difficult-to-copy uniqueness and superior customer values at either p
< 0.01 or 0.05; thus, lending support to H1 - H12. However, the direction of the model effects
differs; across the various recovery instruments, the model effects of on-the-spot recovery
and the indicators of competitive positioning were direct (as one increases, the other increases
also) and for all other recovery instruments and competitive positioning inverse relationship
(when one increases the other decreases) resulted. Further, firm’s technical efficiency directly
moderates the relationship between the categories of recovery instruments and the two
dimensions of competitive positioning, thereby lending support to H13.
Specifically, firms with strong technical efficiency influence the pace of market penetration
through providing enviable basis for building distinctiveness and superior customer values
than firms without such technical expertise. This result shows consistency across existing
studies. Studies (Kwon and Zmud, 1987; Zhu et al., 2003; Alvarez and Crespi, 2003;
Gumbau-Albert and Joaquin, 2002; Davidow, 2000; Maxham and Netemeyer, 2002) suggest
that firms that have higher technical competence show more likelihood to use their
experiential knowledge and best practice models to build competitive advantage in
addressing customer issues. The findings of H1 to H12 show scholarly links with previous
studies. In the context of facilitation, though our finding contrasts those of Blodgett et al.
(1997) and Davidow (2003), which reported that facilitation has no effect or negative effect
on customer values and market distinctiveness; others (Nyer, 2000; Kolodinsky, 1992;
Durvasula et al., 2000) lent support to the finding. While studies (see Ruyter and Wetzels,
2000; Sparks and McColl-Kennedy, 2001) support our finding on redress; Davidow and Leigh
(1998) somewhat contradict it when they reported that redress negatively impacts on
customer value and sales volume.
Further, previous studies supported our finding on timeliness when they found that
timeliness is far more superior to delayed response (Hoffman et al., 1995) and that it
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
impacts positively on corporate image (Clark et al., 1992) and post-complaint attitudes
(Durvasula et al., 2000; Conlon and Murray, 1996). On the contrary, Davidow (2003) and
Estelami (2000) found that timeliness does not impact significantly on sales growth,
customer values, distinctiveness or word-of mouth likelihood; rather what counts most is
what management responds with and how it is expressed. Finally, studies (Prahalad and
Ramaswamy, 2004; Lusch and Vargo, 2004; Bonsu and Darmody, 2008) supported our
finding when they found that user-developer interface impacts significantly customer value,
difficult-to-copy distinctiveness, and market share.
6. Conclusions and Implications
The four dimensions of service recovery studied explained varying relationship with
competitive positioning and led to specific conclusions. Each dimension differs in terms of
its level of statistical interaction and direction of relationship though all were found critical
in determining competitive positioning at p < 0.01 or 0.05. On-the-spot recovery directly
and significantly interacts with competitive positioning; an improved speed of recovery
attracts a corresponding improvement in the customers’ perception of the indicators of
competitive positioning. Further, delayed and/or absence of recovery inversely though
significantly affect the dynamics of the indicators of competitive positioning; increase in
delay or absence of recovery reduces the customers’ perception of the indicators of
competitive positioning. Therefore, the ease with which a disgusted consumer accesses
service providers, registers his complaints, and perhaps interfaces with service providers
and receives hassle-free and timely resolutions impact on measures of competitive
positioning. The statistical interactions between recovery alternatives and measures of
competitive positioning were significantly moderated by firm’s technical efficiency; strong
technical efficiency is more inclined to improving the dimensions of service recovery, and
ultimately provide competitive positioning. The implications of these conclusions are
theoretical and practical.
Theoretically, this paper expands the frontier of knowledge on B2C services and, specifically,
contributes to the growing literature pertaining to telecommunications industry. The
academia is provided with another stream of validated research evidences as well as
extended theory that may stimulate further inquiries and cross-validating. The strength of
the proposed framework was the addition of user interface and competitive positioning,
which seem neglected by previous scholars (see Davidow, 2003; Bitner et al., 1990; Smith et
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
al., 2009; Boshoff, 1999; Johnston and Fern, 1999), as well as correlating the four recovery
alternatives with competitive positioning. Drawing the significance of competitive
positioning from previous scholars (Porter, 1980; Aaker, 1998; Teece, 2000; Thompson et
al., 2004) and that of user interface from a few 18th and 21st century political philosophers
(Prahalad and Ramaswamy, 2004; Lusch and Vargo, 2004; Ogawa and Pillar, 2006) on the
mobilization of communitarian platform to ensure mutual sharing of social knowledge; the
paper validated the correlation between recovery alternatives and competitive positioning.
Aside this being worthwhile for acute dearth of scholarly inquiries that validate the two
extremes, authorities (Newby et al., 1996; Chernoff, 1994; Kozma et al., 1978) suggest that
greater degree of social knowledge sharing stimulates interests, and reinforces learning,
attentiveness, and recalls.
Since all the dimensions of recovery surveyed are critical in determining the behaviour of
competitive positioning, players are practically encouraged to create competitive advantage
through encouraging speedy recovery exercises. The study implies service providers
simplifying recovery procedures and emphasizing interface in addressing customer ordeals;
ease of access to resolution(s); and training and retraining service officers to be proactive
and relational in detecting and dealing with the ordeals. Further, the study points to value
creation and value delivery in the manipulating and regulating recovery instruments since
the study shows that the use of some instruments and their quintiles without actually
improving upon the services inversely correlate with competitive positioning. Finally,
technical efficiency must be continually improved upon since it serves as competitive
weapon that moderate the interactions between recovery instruments and competitive
advantage.
Limitations and suggestions for further studies
The application of the study’s findings is limited by its domain and other factors. First, data
were drawn from one service area or industry to detect the context-specific moderating effect;
thus, the cross-sectional data often imply that the causal relationships identified may vary
across sectors and regions or may even lose meaning overtime. Therefore, extended
measures by cross-validating our scales and/or by engaging in longitudinal study are
required. Second, some errors seemed unavoidable in the SPSS conversion of data just as all
the measures of the constructs represented subjective perceptions and prone to common
error biases (CEBs). Finally, this paper did not study the strength of factors that cause
Proceedings of 4th European Business Research Conference
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service failure and user interface is relatively under-investigated in the context of service
recovery; therefore, further inquiries are encouraged. Though the moderating effects of
environmental complexity and munificence and environmental dynamism have been
investigated outside the context of mobile telephony, other studies may factor them into
service recovery frameworks.
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